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Convergent methodologies in prosthetic joint infection research: integrating transdisciplinary approaches to understand and prevent biofilm-driven failure of orthopaedic prostheses.

Prosthetic joint infections (PJIs) remain among the most devastating complications of arthroplasty, imposing substantial clinical, economic and patient burdens. Although culture-based diagnostics underpin current clinical practice, PJIs are biofilm-driven infections shaped by taxonomic diversity, spatial organization, host responses and surface interactions, meaning conventional approaches provide only a partial and often decontextualized view of the infection process. We examine how convergent methodologies can transform PJI research by integrating approaches that have traditionally been studied in isolation, including sequencing, transcriptomics, metabolomics, advanced imaging and culture-based characterization. We discuss how whole-genome sequencing, shotgun metagenomics, transcriptomic and metabolomic approaches resolve pathogen identity, functional activity and adaptive persistence and how cross-scale imaging and spatial biology techniques reveal where microbes colonize, interact and survive across implant surfaces. We highlight emerging opportunities to unify these datasets into coherent frameworks that capture both the molecular and physical dimensions of PJIs. Integrating these complementary approaches will enable a multi-layered understanding of PJIs that link composition, function and spatial organization. Ultimately, this provides a foundation for predictive diagnostics, precision antimicrobial strategies and improved implant design and supports a shift towards more effective, mechanism-informed management of implant-associated infection.

Prosthesis-Related Infections

Downregulation of Trpv4 and Klf2 in brain microvessels is associated with the progression of neurovascular dysfunction and cognitive impairment in a model of heart failure with preserved ejection fraction.

Vascular cognitive impairment (VCI) shares major risk factors with heart failure with preserved ejection fraction (HFpEF), including obesity, diabetes and hypertension. Yet VCI research often relies on single-stimulus models, whereas patients experience combined risk factors. We therefore assessed cerebrovascular and cognitive phenotypes in an HFpEF model and investigated underlying mechanisms. Male Lean and Obese ZSF1 rats underwent longitudinal assessments of blood pressure, glucose, cardiac function and behavioural performance. Cerebral blood flow and neurovascular coupling were assessed by laser speckle contrast imaging. White matter integrity, blood-brain barrier (BBB) permeability and vascular density were analyzed by (immuno)histochemistry. Cortical microvessels were isolated for transcriptomic profiling, and selected targets were validated using multiplex in-situ hybridization. Obese rats exhibited neurovascular uncoupling and impaired short- and long-term memory and spatial learning, accompanied by brain atrophy and reduced myelin. BBB permeability increased at 22-23 weeks and vascular density at 34-35 weeks in Obese versus Lean rats. Transcriptomic analysis of brain microvessels revealed altered processes related to angiogenesis, vasoreactivity, immune mechanisms and vascular remodelling, with consistent downregulation of Trpv4 and Klf2. Obese ZSF1 rats develop progressive neurovascular dysfunction associated with HFpEF onset and reduced Trpv4 and Klf2 expression in cerebral microvessels, two key vasoprotective genes.

Diastolic dysfunction

Genomic Structural Equation Modeling Identifies a Shared Inflammatory Genetic Dimension Across Inflammatory Arthritis Phenotypes and Biomarkers.

BACKGROUND: Inflammatory arthritis (IA), including rheumatoid arthritis (RA), psoriatic arthritis (PsA) and gout, shares systemic inflammatory features indexed by C-reactive protein (CRP) and interleukin-6 (IL-6), yet the extent of their common genetic basis remains unclear. AIMS: We aimed to delineate the shared genetic architecture across IA phenotypes and inflammatory biomarkers. MATERIALS AND METHODS: We applied genomic structural equation modelling (Genomic SEM) to GWAS summary statistics for RA, PsA, gout, CRP and IL-6, fitted a single common factor, and performed multivariate GWAS followed by fine-mapping, transcriptome-wide association, gene-based analysis, pathway enrichment, and cell-type and spatial mapping. RESULTS: A single common factor was fitted (CFI = 0.990, SRMR = 0.045). The multivariate GWAS identified 56 genome-wide significant SNPs across 10 independent lead loci, including one novel signal. Fine-mapping prioritized high-confidence variants near PTPN22, the CRP gene cluster and a urate-associated locus. Gene-level analyses converged on DCLRE1B, PTPN22, IL6R, NLRP3 and HNF1A, with pathway enrichment implicating inflammasome assembly and metabolic-inflammatory overlap. Cell-type enrichment highlighted myeloid populations, and spatial mapping localized signals to lung, kidney, mucosal epithelium and gastrointestinal tissues. DISCUSSION: These results delineate a shared inflammatory genetic dimension across IA phenotypes and biomarkers, anchored in immune, inflammasome, cytokine-receptor and metabolic pathways. CONCLUSION: Together, these findings provide a valuable framework for prioritizing candidate genes and cellular contexts for future investigation.

TWAS

Multiomics approaches to cardiovascular disease: technological innovations and clinical translation.

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.

Humans

Spatial habitat radiomics predicts tertiary lymphoid structure status and identifies an IDO1+ migratory dendritic cell axis in breast cancer.

BACKGROUND: Tertiary lymphoid structures (TLS) are spatially organized immune niches associated with therapeutic response and favorable outcomes in breast cancer (BC). However, TLS assessment currently relies on invasive tissue-based analyses, and the biological mechanisms underlying imaging-based TLS prediction remain poorly understood. METHODS: We developed and validated a spatial heterogeneity-based radiomic TLS signature (shTLS) using dynamic contrast-enhanced MRI to non-invasively predict TLS status across multicenter BC cohorts. Spatial habitat radiomics were used to capture intratumoral and peritumoral immune-related heterogeneity. Integrated multi-omics analyses, including transcriptomics, pathomics, genomics, single-cell RNA sequencing, immunohistochemistry, and multiplex immunofluorescence, were performed to biologically interpret shTLS-defined subgroups. Functional drug-sensitivity assays were conducted to assess therapeutic implications. RESULTS: The shTLS model achieved robust predictive performance across independent cohorts and molecular subtypes. High shTLS scores were associated with immune-inflamed tumors characterized by spatially clustered activated T cells and dendritic cells (DCs). In contrast, shTLS-low tumors exhibited an immunosuppressive spatial niche with peripheral accumulation of CD4+ PD-1+ T cells and plasma cells, increased immune-tumor separation, and enhanced inflammatory and immunoregulatory signaling. An indoleamine 2,3-dioxygenase 1 (IDO1)-associated immunoregulatory program was observed in the shTLS-low tumors, which appeared to be preferentially expressed by LAMP3+CCR7+ migratory DCs. Pharmacologic inhibition of IDO1 enhanced chemotherapy and CDK4/6 inhibitor sensitivity in vitro. CONCLUSION: This study establishes spatial radiomics as a non-invasive approach to decode TLS-associated immune ecosystems and supports the presence of an IDO1-associated immunosuppressive phenotype, providing biological insight and translational rationale for patient stratification and future combination strategies.

Humans

Dissecting spatial heterogeneity and the immune-evasion mechanism of CTCs by single-cell RNA-seq in hepatocellular carcinoma.

Little is known about the transcriptomic plasticity and adaptive mechanisms of circulating tumor cells (CTCs) during hematogeneous dissemination. Here we interrogate the transcriptome of 113 single CTCs from 4 different vascular sites, including hepatic vein (HV), peripheral artery (PA), peripheral vein (PV) and portal vein (PoV) using single-cell full-length RNA sequencing in hepatocellular carcinoma (HCC) patients. We reveal that the transcriptional dynamics of CTCs were associated with stress response, cell cycle and immune-evasion signaling during hematogeneous transportation. Besides, we identify chemokine CCL5 as an important mediator for CTC immune evasion. Mechanistically, overexpression of CCL5 in CTCs is transcriptionally regulated by p38-MAX signaling, which recruites regulatory T cells (Tregs) to facilitate immune escape and metastatic seeding of CTCs. Collectively, our results reveal a previously unappreciated spatial heterogeneity and an immune-escape mechanism of CTC, which may aid in designing new anti-metastasis therapeutic strategies in HCC.

Aged

Stereo-cell: Spatial enhanced-resolution single-cell sequencing with high-density DNA nanoball-patterned arrays.

Single-cell sequencing technologies have advanced our understanding of cellular heterogeneity and biological complexity. However, existing methods face limitations in throughput, capture uniformity, cell size flexibility, and technical extensibility. We present Stereo-cell, a spatial enhanced-resolution single-cell sequencing platform based on high-density DNA nanoball (DNB)-patterned arrays, which enables scalable and unbiased cell capture at a wide input range and supports high-fidelity transcriptome profiling. Stereo-cell further allows integration with imaging-based modalities and multiomics strategies, including immunofluorescence and epitope profiling. This platform is also compatible with profiling extracellular vesicles, microstructures, and large cells, whereas its spatial resolution facilitates in situ analysis of cell-cell interactions, cellular microenvironments, and subcellular transcript localization. Together, Stereo-cell provides a flexible framework for expanding single-cell research applications.

Animals

Single-cell transcriptomic landscape of the southern green stink bug (Nezara viridula) midgut.

BACKGROUND: The southern green stink bug (SGSB), Nezara viridula, is a globally distributed hemipteran pest that damages many economically important crops. Its midgut supports digestion, defense, symbiosis, and interactions with orally delivered control agents, yet the cellular composition of this tissue remains poorly characterized. We therefore developed a single-cell transcriptomic atlas of the N. viridula midgut. RESULTS: Single-cell RNA sequencing of two biological replicates yielded a quality-filtered data set of 13,763 cells. Unsupervised clustering identified 12 transcriptionally distinct populations with putative annotations, including a stem cell/enteroblast (SC/EB)-like population, seven enterocyte-related populations, goblet-like cells, enteroendocrine cells, visceral muscle cells, and an extracellular-matrix-associated epithelial population. Enterocyte-related populations accounted for more than 77% of recovered cells. Putative annotations were assigned primarily from marker gene enrichment and homology to markers reported in other insects. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses identified population-associated functional enrichment patterns, and pseudotime analysis suggested transcriptional relationships between the SC/EB-like population and several enterocyte- and secretory-associated populations without establishing developmental lineages. Immune- and defense-associated transcripts were preferentially enriched in the pEC2 population, and genes associated with symbiont recognition, insecticide action, xenobiotic transport, and orally delivered double-stranded RNA showed population-biased expression. Descriptive comparisons with published insect midgut data sets identified shared and data-set-specific patterns among annotated populations. CONCLUSION: This atlas provides the first single-cell transcriptomic resource for a stink bug midgut and establishes a descriptive cellular framework for SGSB midgut biology. The dataset prioritizes candidate genes and cell populations for future spatial validation, functional testing, and studies of hemipteran midgut physiology, symbiosis, immunity, and pest-management-relevant traits. © 2026 Society of Chemical Industry.

Nezara viridula

Widespread release of translational repression across Plasmodium's host-to-vector transmission event.

Malaria parasites must respond quickly to environmental changes, including during their transmission between mammalian and mosquito hosts. Therefore, female gametocytes proactively produce and translationally repress mRNAs that encode essential proteins that the zygote requires to establish a new infection. While the release of translational repression of individual mRNAs has been documented, the details of the global release of translational repression have not. Moreover, changes in the spatial arrangement and composition of the DOZI/CITH/ALBA complex that contribute to translational control are also not known. Therefore, we have conducted the first quantitative, comparative transcriptomics and DIA-MS proteomics of Plasmodium parasites across the host-to-vector transmission event to document the global release of translational repression. Using female gametocytes and zygotes of P. yoelii, we found that ~200 transcripts are released for translation soon after fertilization, including those encoding essential functions. Moreover, we identified that many transcripts remain repressed beyond this point. TurboID-based proximity proteomics of the DOZI/CITH/ALBA regulatory complex revealed substantial spatial and/or compositional changes across this transmission event, which are consistent with recent, paradigm-shifting models of translational control. Together, these data provide a model for the essential translational control mechanisms that promote Plasmodium's efficient transmission from mammalian host to mosquito vector.

Animals

Evolutionary and resistance dynamics in oligometastatic and oligoprogressive cancer treated with stereotactic radiotherapy and systemic therapies: A systematic review and focused meta-analysis.

BACKGROUND: Oligometastatic and oligoprogressive disease treated with stereotactic ablative radiotherapy (SABR) represents a clinically heterogeneous entity. Increasing evidence suggests that anatomical definitions alone may not adequately capture underlying biological diversity. This systematic review aimed to synthesize translational evidence exploring evolutionary dynamics, resistance mechanisms, and biomarker-driven stratification in patients treated with SABR. METHODS: A systematic literature review was performed including prospective and retrospective studies evaluating translational biomarkers in oligometastatic or oligoprogressive settings treated with SABR. Studies assessing genomic, transcriptomic, circulating or immune-related biomarkers were included. Data were summarized qualitatively according to predefined translational domains: (i) evolutionary dynamics under systemic therapy pressure, (ii) baseline biological stratification, (iii) longitudinal circulating biomarkers, and (iv) systemic immune remodeling. Exploratory quantitative visual syntheses were performed using reported hazard ratios when conceptually comparable endpoints were available. RESULTS: 19 studies comprising 1527 patients were included. Across tumor types and treatment contexts, translational analyses consistently indicated that anatomically defined oligometastatic states encompass biologically distinct subgroups with different risks of systemic progression. Studies evaluating oligoprogression under ongoing systemic therapy suggested a distinction between spatially constrained resistance and systemic molecular escape, supported by circulating tumor DNA and tissue- or plasma-based molecular profiling (including genomic and transcriptomic analyses). Baseline biological features, including adverse genomic signatures and circulating biomarkers, were associated with inferior progression outcomes despite metastasis-directed therapy. Longitudinal biomarkers provided early signals of treatment response and systemic control. Immune remodeling after SABR showed context-dependent effects, both systemic immune activation and treatment-related immunosuppression reported across studies.

Humans

Multi-sampling allows intra-tumoral heterogeneity querying and vulnerability profiling in glioblastoma.

BACKGROUND: Glioblastoma (GBM) remains a devastating cancer with limited treatment options, largely due to its heterogeneity. While supramaximal resection has recently provided survival benefits, therapeutic profiling of different tumor compartments, particularly its infiltrative edge remains largely unexplored. METHODS: Here, we leveraged magnetic resonance imaging (MRI)-guided multi-sampling, collecting 2 cores and 2 margins per case, to query GBM heterogeneity. Whole-exome and RNA-seq with drug testing in two patient-derived 3D models were used to reveal similarities and differences in genomic and transcriptomic makeups, cellular compositions, and drug responses across cores and margins. Bioinformatics interrogations further identified response biomarkers. RESULTS: Mutation analysis showed that oncogenes exhibited a higher degree of spatial heterogeneity than tumor suppressor genes, regardless of MRI status. While the mesenchymal transcriptional subtype with extracellular matrix remodeling, stress response, and immune programs were preferentially enriched in enhancing cores, proneural tumors with neurological processes favored non-enhancing margins. Using a 15-drug GBM-targeted panel, ERK (ulixertinib) and PI3K pathway (paxalisib, CC-115) inhibitors showed preferential efficacy in enhancing cores and non-enhancing margins, respectively. The anti-apoptosis, pan-Bcl2 agent navitoclax and the epigenetic drug trotabresib represented the most effective, tumor-wide monotherapies. Importantly, drug combinations generally outperformed single agents across all regions. CONCLUSIONS: This work demonstrates the regional heterogeneity of therapeutic vulnerabilities in GBM ex vivo, showing various drugs with tumor-wide or MRI-enhancement informed activity. These findings offer preclinical bases of numerous monotherapies and drug combinations for future clinical trial design.

Humans

Human IL-34 Deficiency Primes Microglia Toward Alzheimer's Disease-Associated States.

BACKGROUND: Genome-wide association studies (GWAS), with independent replication in large European consortia, have identified a common nonsense variant in IL-34 (Y213X) as a genetic risk factor for late-onset Alzheimer's disease (AD). However, the biological consequences of this IL-34 mutation in humans, its prevalence in the population, and the mechanisms by which IL-34-Y213X alters microglial homeostasis, cerebrospinal fluid (CSF) proteomic networks, and amyloid pathology remain poorly understood. METHODS: We combined human genetics, cerebrospinal fluid (CSF) and serum proteomics, transcriptomics, large-scale phenome-wide association analyses, and preclinical experimental models to define the impact of human IL-34 deficiency. IL-34 concentrations were first quantified in CSF and serum from deeply phenotyped AD cohorts stratified by the common IL-34-Y213X nonsense variant. IL-34 levels and IL-34-Y213X status were then integrated with unbiased CSF proteomic networks and AD biomarkers. Transcriptomic profiling of purified microglia from IL-34 knockout mice was performed to assess disease-associated microglial programs. Using APP/PS1 mice lacking IL-34, we examined the effects of IL-34 deficiency on microglial survival, tiling, and plaque encapsulation. Finally, we performed postmortem analyses of temporal cortex from AD patients carrying IL-34-Y213X to assess microglial density, spatial organization, and plaque-associated responses. FINDINGS: IL-34-Y213X was a strong, dose-dependent loss-of-function (LOF) allele that reduced IL-34 levels by up to 2.5 standard deviations in CSF and serum and was common in multiple populations. IL-34 deficiency reshaped CSF proteomic networks, downregulating axon guidance and microglial support modules while upregulating inflammatory and extracellular matrix signatures, and showed pleiotropic associations with neurological, inflammatory, and metabolic traits. Transcriptomic analysis of sorted microglia from healthy 9-month-old IL-34KO compare to wild-type mice revealed a profound pro-inflammatory and disease-associated microglial transcriptional program enriched for disease-associated microglia (DAM) signatures, inflammatory pathways, and AD risk genes including APOE, CLU, and CASS4. In APP/PS1 mice, genetic IL-34 deletion selectively depleted homeostatic gray-matter microglia, disrupted microglial tiling, and impaired plaque encapsulation, resulting in altered amyloid structure and enhancing neuritic injury. Concordantly, AD patients homozygous for IL-34-Y213X displayed markedly reduced cortical microglial density and increased microglial spatial dispersion, indicating a breakdown of the microglial network organization in the human brain. INTERPRETATION: A common human IL-34 LOF variant creates a naturally occurring model of IL-34 deficiency that links microglial survival, CSF network signatures, and amyloid pathology in both mice and humans. Importantly, IL-34 deficiency alone is sufficient to induce inflammatory, AD-associated microglial states beyond simply reducing microglial number. These findings identify IL-34/CSF1R signaling as a critical determinant of microglial resilience and a potential upstream pathway linking human genetic variation to AD susceptibility, highlighting IL-34-dependent pathways as promising targets for disease modification. FUNDING: This work was supported by grants from the Spanish Ministerio de Ciencia, Innovación y Universidades/FEDER/UE (PID2024-157400OB-I00) and FORTALECE program (FORT23/00008; Instituto de Salud Carlos III, Spain) to RRL and JLV, ISCIII of Spain co-financed by FEDER funds (European Union) through grants PI24/00308 (JV) and CIBERNED collaborative grant 2022/01 to JV, PID2023-147125OB-I00 and CEX2023-001386-S (Severo Ochoa Programme) to SMTBC. A.R. is supported by STAR Award. University of Texas System. Tx, United States, The South Texas ADRC. National Institute of Aging. National Institutes of Health. USA. (P30AG066546), the Keith M. Orme and Pat Vigeon Orme Endowed Chair in Alzheimer's and Neurodegenerative Diseases (2024-2025) and Patricia Ruth Frederick Distinguished Chair for Precision Therapeutics in Alzheimer's and Neurodegenerative Diseases (2025-2028). AR is also supported by the Agency for Innovation and Entrepreneurship (VLAIO) grant N° PR067/21 for the HARPONE project and the ADAPTED project the EU/EFPIA Innovative Medicines Initiative Joint Undertaking Grant N° 115975 and CIBERNED (ISCIII).

Journal Article

New Genetic Loci Implicated in Cardiac Morphology and Function Using Three-Dimensional Population Phenotyping.

BACKGROUND: Cardiac remodeling occurs in the mature heart and is a cascade of adaptations in response to stress, which are primed in early life. A key question remains as to the processes that regulate the geometry and motion of the heart and how it adapts to stress. METHODS: We performed spatially resolved phenotyping using machine learning-based analysis of cardiac magnetic resonance imaging in 47 549 UK Biobank participants. We analyzed 16 left ventricular spatial phenotypes, including regional myocardial wall thickness and systolic strain in both circumferential and radial directions. In up to 40 058 participants, genetic associations across the allele frequency spectrum were assessed using genome-wide association studies with imputed genotype participants, and exome-wide association studies and gene-based burden tests using whole-exome sequencing data. We integrated transcriptomic data from the GTEx project and used pathway enrichment analyses to further interpret the biological relevance of identified loci. To investigate causal relationships, we conducted Mendelian randomization analyses to evaluate the effects of blood pressure on regional cardiac traits and the effects of these traits on cardiomyopathy risk. RESULTS: We found 42 loci associated with cardiac structure and contractility, many of which reveal patterns of spatial organization in the heart. Whole-exome sequencing revealed 3 additional variants not captured by the genome-wide association study, including a missense variant in CSRP3 (minor allele frequency 0.5%). The majority of newly discovered loci are found in cardiomyopathy-associated genes, suggesting that they regulate spatially distinct patterns of remodeling in the left ventricle in an adult population. Our causal analysis also found regional modulation of blood pressure on cardiac wall thickness and strain. CONCLUSIONS: These findings provide a comprehensive description of the pathways that orchestrate heart development and cardiac remodeling. These data highlight the role that cardiomyopathy-associated genes have on the regulation of spatial adaptations in those without known disease.

Humans

Spatial genomics: Mapping the landscape of fibrosis.

Organ fibrosis causes major morbidity and mortality worldwide. Treatments for fibrosis are limited, with organ transplantation being the only cure. Here, we review how various state-of-the-art spatial genomics approaches are being deployed to interrogate fibrosis across multiple organs, providing exciting insights into fibrotic disease pathogenesis. These include the detailed topographical annotation of pathogenic cell populations and states, detection of transcriptomic perturbations in morphologically normal tissue, characterization of fibrotic and homeostatic niches and their cellular constituents, and in situ interrogation of ligand-receptor interactions within these microenvironments. Together, these powerful readouts enable detailed analysis of fibrosis evolution across time and space.

Humans

Next-Generation Disease Profiling by Integrating Histopathology with Spatial Multi-Omics Data.

The field of pathology has experienced several transformative changes in recent years with the advent of digital pathology and spatial multi-omics. These technologies have enhanced every aspect of pathology practice, from streamlining daily workflows to generating high-fidelity multi-omics data that provide pathologists with novel tools to refine disease profiling and clinical diagnosis. Each layer of multimodal data (genomic, metabolomic, proteomic, or transcriptomic) has uncovered a distinct facet of disease pathologies, and combined with machine learning/artificial intelligence-based data analysis and pattern recognition models, has provided holistic understanding of regulatory mechanisms underpinning them. However, high-dimensional data have far exceeded the volume, scale, and complexity of immunostaining methods implemented by pathologists and, thus, have generated significant challenges related to deconvolution, interpretation, and clinical translation. Furthermore, these multimodal studies have predominantly relied on computational methods to process data and extract disease-relevant insights, thus raising questions around relevance or role of a pathologist in this new era of multi-omics. This review will provide a perspective on the evolving fields of molecular histopathology and spatial -omics, leveraging them to approach disease profiling, and redefining the role of a pathologist during this process.

Humans

Dual-patterned pluripotent stem cells self-organize into a human embryo model with extended anterior-posterior patterning.

Human gastruloids are a powerful class of stem cell-derived models that recapitulate key features of early embryonic development, including symmetry breaking and the emergence of three germ layers1-3. However, they lack anterior embryonic structures and coordinated axial organization4-6. To address this limitation, we pre-patterned human pluripotent stem cells (hPSCs) by exposing them to either anterior (FGF2) or posterior (CHIR99021 [CHIR] & retinoic acid [RA]) cues. Upon mixing, these dual-patterned hPSCs interacted and self-organized into elongated structures with both anterior and posterior features-which we term anterior-posterior (AP) human gastruloids. Anteriorly pre-treated cells robustly intercalated into posteriorly pre-treated cells, collectively giving rise to a continuum of neural tissues-including a brain-like domain, a neural tube-like structure, and neuro-mesodermal progenitors (NMPs)-with segmented somites arrayed bilaterally. Single cell RNA sequencing (scRNA-seq) revealed that human AP gastruloids contain cell types resembling the midbrain-hindbrain boundary (MHB), regionalized hindbrain structures (i .e. rhombomeres 1-8), regionalized neural crest (i.e. cranial, vagal, trunk)7,8 and head mesoderm. Transcriptomic comparisons to primate embryos revealed that human AP gastruloids most closely resemble Carnegie stage 11 (CS11) embryos. While they lack a notochord and full dorsal-ventral polarity, human AP gastruloids recapitulate key spatial and temporal features of early neurulation and somitogenesis. Perturbation of folic acid metabolism or rho-associated kinase (ROCK) signaling induced spinal cord defects, phenocopying aspects of spina bifida and other neural tube defects, highlighting this model's potential for studying congenital disorders9. AP gastruloids may serve as a simple, robust, scalable platform for modeling coordinated human AP body axis development. More broadly, our results suggest that controlled interactions between differentially prepatterned progenitors can initiate self-organization of complex body axis features. The "pattern-and-mix" strategy may serve as a generalizable framework for assembling spatially organized stem cell models of mammalian development.

Journal Article

Metab8D: a metabolic regulome network from multiomics and machine learning.

To explore multiomic regulation of the metabolome, we used machine learning to predict metabolomic variation across ~1000 different cancer cell lines with matched omics data from eight biomolecular classes: genomic copy number variation, mutations, DNA methylation, histone post-translational modifications (PTMs), transcriptomics and RNA splice variants, non-coding transcriptomics (miRNA and lncRNA), proteomics, and phosphoproteomics. Overall, the metabolome is tightly associated with the transcriptome, with coding and non-coding RNAs emerging as top predictors. Peripheral metabolites are predictable via levels of corresponding enzymes, while those in central metabolism require combinatorial predictors in signaling and redox pathways, and may not reflect corresponding pathway expression. We reconstruct multiomic interaction subnetworks for highly predictable metabolites, and YAP1 signaling emerged as a top global predictor across four omic layers. We prioritize predictive multiomic features for single-cell and spatial metabolomics assays. Top predictors were enriched for synthetic-lethal interactions and synergistic combination therapies that target compensatory metabolic modulators.

Machine Learning

Imaging-Guided Omics Technologies for Resolving Rare Cancer States and Advancing Nanomedicine.

The ability to resolve rare and transient cellular states is critical for understanding metastasis, immune evasion, and therapy resistance in cancer, yet these dynamic processes often escape detection by conventional sequencing and imaging approaches. Recent advances at the interface of nanotechnology, high-resolution live-cell imaging, and single-cell/spatial multiomics methods have enabled functional profiling of cells with unprecedented precision within their native microenvironment. In this Mini-Review, we highlight emerging nanoscale platforms that couple real-time phenotypic imaging with molecular readouts, such as FUNseq and CIN-seq, to directly link functional heterogeneity to transcriptomic, proteomic, and epigenomic information. By integrating nanoscale optical imaging, microengineered perturbation tools, and AI-driven computational analysis, these technologies open up new avenues for dissecting rare metastatic, therapy-resistant, or immune-evasive subpopulations. We further discuss how these next-generation imaging-guided single-cell and spatial omics platforms not only advance fundamental cancer biology but also create opportunities to accelerate the development of nanomedicine applications.

Humans